基于组合矩和随机森林的转子轴心轨迹识别

针对大型旋转机械难以获得大量故障样本和不变矩识别率低的问题，提出基于组合矩和随机森林模型的转子轴心轨迹识别方法。采用实测的轴心轨迹作为样本，采用Sobel算子提取轴心轨迹的轮廓，基于轮廓的形状几何特征和不变矩构造组合矩。将不变矩和组合矩作为随机森林模型的输入进行分类，证明了组合矩的分类准确率最高。对随机森林、支持向量机和BP神经网络的分类效果进行了对比，结果表明：随机森林的分类准确率要高于支持向量机和BP神经网络，并且识别时间较短，是诊断旋转机械故障的一种新方法

Identification of Rotor Shaft Orbit Based on Combined Moment Invariants and Random Forest

Aimed at the problem that it is difficult to obtain a large number of fault samples for large rotating machinery and the recognition rate of moment invariance is low, an identification method for rotor shaft orbit based on combined moment and stochastic forest model was proposed.The measured shaft orbit was taken as the sample,Sobel operator was used to extract the contour of the shaft orbit,and the combined moment was constructed based on the geometric features of the shape and the moment invariant.The moment invariants and the combined moments were used as input of the random forest model to classify,and the classification accuracy of the combined moments was the highest.The classification results of random forest, support vector machine (SVM) and BP neural network were compared.The results show that the classification accuracy of random forest is higher than that of SVM and BP neural network, and the identification time is shorter, so it is a new method to diagnose the fault of rotating machinery